Robust design optimization of gas turbine compression systems

Gas turbine compression systems are required to perform adequately over a range of operating conditions. Complexity has encouraged the conventional design process for compressors to focus initially on one operating point, usually the most commonor arduous, to draw up an outline design. Generally, only as this initial design is refined is its offdesign performance assessed in detail. Not only does this necessarily introduce a potentially costly and timeconsuming extra loop in the design process, but it also may result in a design whose offdesign behavior is suboptimal. Aversion of nonintrusive polynomial chaos was previously developed in which a set of orthonormal polynomials was generated to facilitate a rapid analysis of robustness in the presence of generic uncertainties with good accuracy. In this paper, this analysis method is incorporated in real time into the design process for the compression system of a three-shaft gas turbine aeroengine. This approach to robust optimization is shown to lead to designs that exhibit consistently improved system performance with reduced sensitivity to offdesign operation.

Solomon Islands Aquatic Agricultural Systems Program design document

WorldFish is leading the CGIAR Research Program on Aquatic Agricultural Systems together with two other CGIAR Centers; the International Water Management Institute (IWMI) and Bioversity. In 2012 and 2013 the AAS Program rolled out in Solomon Islands, Zambia, Bangladesh, Cambodia and the Philippines. Aquatic Agricultural Systems are places where farming and fishing in freshwater and/or coastal ecosystems contribute significantly to household income and food security. The program goal is to improve the well-being of AAS-dependent people. A hub is a geographic location that provides a focus for learning, innovation and impact through participatory action research. In Solomon Islands AAS works in Malaita Hub (Malaita Province) and Western Hub (Western Province). In each hub we identify a ‘Development Challenge’ that the Program will address to give us focus and motivation.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.